Several in vivo measurement systems are known in the art. They include swallowable electronic capsules which collect data and which transmit the data to a receiver system. These intestinal capsules, which are moved through the digestive system by the action of peristalsis, are used to measure pH (“Heidelberg” capsules), temperature (“CoreTemp” capsules) and pressure throughout the gastro-intestinal (GI) tract. They have also been used to measure gastric residence time, which is the time it takes for food to pass through the stomach and intestines. These intestinal capsules typically include a measuring system and a transmission system, where a transmitter transmits the measured data at radio frequencies to a receiver system.
U.S. Pat. No. 5,604,531, assigned to the State of Israel, Ministry of Defense, Armament Development Authority, and incorporated herein by reference, teaches an in vivo measurement system, in particular an in vivo camera system, which is carried by a swallowable capsule. In addition to the camera system there is an optical system for imaging an area of the GI tract onto the imager and a transmitter for transmitting the video output of the camera system. The overall system, including a capsule that can pass through the entire digestive tract, operates as an autonomous video endoscope. It also images the difficult to reach areas of the small intestine.
FIG. 1 (prior art) shows a block diagram of the in vivo video camera system described in U.S. Pat. No. 5,604,531. The system captures and transmits images of the GI tract while passing through the gastro-intestinal lumen. The system contains a storage unit 100, a data processor 102, a camera 104, an image transmitter 106, an image receiver 108, which usually includes an antenna array, and an image monitor 110. Storage unit 100, data processor 102, image monitor 110, and image receiver 108 are located outside the patient's body. Camera 104, as it transits the GI tract, is in communication with image transmitter 106 located in capsule 112 and image receiver 108 located outside the body. Data processor 102 transfers frame data to and from storage unit 100 while analyzing the data. Processor 102 also transmits the analyzed data to image monitor 110 where a physician views it. The data can be viewed in real time or at some later date.
During a typical examination, the in vivo camera system may take anywhere from about four to eight hours or more to traverse the digestive tract. Assuming a capture rate of about 2 images per second, the total number of captured images can range from approximately 35,000 to 70,000 or more images. If these images were subsequently displayed as a video sequence at a rate of 30 frames per second, one would require 20-40 minutes of viewing time to observe the entire video. This estimate does not include the extra time needed to zoom in and/or decrease the frame rate for a more detailed examination of suspect areas. In practice, the total time required to interpret an in vivo examination can range from upwards of 20 minutes to four hours.
One frequent frustration of medical practitioners who interpret in vivo examinations is that there may be long time periods where fecal matter or partially digested food adheres to the optical dome of the capsule, rendering as useless the images captured during those time periods. This can occur if the patient does not fast for a long enough period of time prior to examination. Such a situation does not necessarily render the entire in vivo image sequence unusable, however, because matter adhering to the optical dome may eventually be removed by contact with the walls of the GI tract. In current practice, when fecal matter or partially digested food obscures portions of the image sequence, the medical practitioner must cue through the video in search of usable images. This cueing procedure can use valuable time, causing some practitioners to abandon the entire examination out of frustration. A similar cause of frustration occurs when the quality of imagery is so low in segments of the image sequence that there is no diagnostically useful information. One example of this occurrence is when the in vivo capsule is inside a body lumen such as the stomach or colon, and the illumination power is not high enough to illuminate the lumen wall, resulting in underexposed images.
Images that are unusable, whether obstructed by fecal matter, partially digested food, or due to poor image quality, are denoted as undesirable. In the context of viewing and/or interpreting image sequences, a single undesirable image or frame, or a few undesirable images or frames, may not be very frustrating, especially if the image sequence is being viewed at a rate of multiple frames per second. A problem occurs when a substantial number of undesirable images occur within some portion of the image sequence. For example, ten undesirable images in sequence may be viewed in seconds and would not likely be thought of as frustrating; but, five thousand undesirable images in sequence may require minutes to view, causing the medical practitioner to cue the sequence or abandon the examination. There is no clear-cut bright line threshold on the minimum number of undesirable images required to cause the viewer substantial frustration; in fact, this number is viewer dependent and probably varies for a single viewer depending on his/her tiredness.
Some prior art exists on automatically determining desirability of an image. Savakis and Loui, in commonly assigned U.S. Pat. No. 6,535,636, issued Mar. 18, 2003, teach a method for automatically classifying an image as undesirable based on thresholding one or more of the computed image characteristics that include sharpness, contrast, noise and exposure. While this method is adequate for classifying individual images as undesirable, it does not readily or efficiently extend to classifying groups of images, or portions of the image sequence, as undesirable. In the context of in vivo examinations, individual images belong to an image sequence. One simple and obvious way to apply the Savakis and Loui patent within this context is to detect individual images within the image sequence that are undesirable, and then determine whether or not any subsequences of undesirable images exist with at least a given minimum length. However, such a method could easily fail in circumstances where classification is difficult (i.e., where computed image characteristics exhibit values near the thresholds). In addition, such a method would tend to be inefficient because it fails to exploit interrelationships between the image characteristics of nearby images in the sequence. Therefore, there remains a need in the art for an improved method and system for automatically detecting undesirable images within an image sequence.